A Locally Adaptive Weighting and Screening Approach To Spatial Multiple Testing

主讲人:夏寅
主讲人简介:

复旦大学管理学院统计与数据科学系教授,博导,2013年博士毕业于宾夕法尼亚大学,2013-2016年在美国北卡大学教堂山分校任tenure-track助理教授。2020年获得国家自科基金优秀青年基金资助。研究方向包括高维统计推断、大范围检验及应用等。在JASA, AOS, JRSSB, Biometrika等期刊上发表二十余篇论文。

主持人:刘婧媛
简介:

Exploiting spatial patterns in large-scale multiple testing  promises to improve both power and interpretability of false discovery rate (FDR) analyses. This talk develops a new class of locally-adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of 2D and 3D images.

时间:2022-09-29(Thursday)16:40-18:20
地点:线上腾讯会议
讲座语言:中文
期数:
主办单位:厦门大学经济学院、王亚南经济研究院、邹至庄经济研究院
承办单位:厦门大学经济学院统计学与数据科学系
类型:独立讲座
专题网站:
联系人信息:周梦娜,电话:2182886

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